2 code implementations • ICLR 2018 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual's potential outcomes to be learned from biased data and without having access to the counterfactuals.
Ranked #2 on Causal Inference on Jobs
2 code implementations • ICML 2018 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
Training complex machine learning models for prediction often requires a large amount of data that is not always readily available.
8 code implementations • ICML 2018 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN).
1 code implementation • 29 Jun 2018 • James Jordon, Jinsung Yoon, Mihaela van der Schaar
Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available.
1 code implementation • ICLR 2019 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available.
Ranked #2 on Synthetic Data Generation on UCI Epileptic Seizure Recognition (using extra training data)
1 code implementation • ICLR 2019 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
The advent of big data brings with it data with more and more dimensions and thus a growing need to be able to efficiently select which features to use for a variety of problems.
1 code implementation • ICLR 2019 • James Jordon, Jinsung Yoon, Mihaela van der Schaar
We demonstrate the capability of our model to perform feature selection, showing that it performs as well as the originally proposed knockoff generation model in the Gaussian setting and that it outperforms the original model in non-Gaussian settings, including on a real-world dataset.
no code implementations • 29 May 2019 • Yao Zhang, James Jordon, Ahmed M. Alaa, Mihaela van der Schaar
In this paper, we present Lifelong Bayesian Optimization (LBO), an online, multitask Bayesian optimization (BO) algorithm designed to solve the problem of model selection for datasets arriving and evolving over time.
1 code implementation • 16 Jun 2019 • Jinsung Yoon, James Jordon, Mihaela van der Schaar
The predictor network uses the observations selected by the selector network to predict a label, providing feedback to the selector network (well-selected variables should be predictive of the label).
1 code implementation • NeurIPS 2019 • James Jordon, Jinsung Yoon, Mihaela van der Schaar
The second benefit is that, through analysis that we provide inthe paper, we can derive tighter differential privacy guarantees when several queriesare made to this mechanism.
no code implementations • ICLR 2020 • Ioana Bica, James Jordon, Mihaela van der Schaar
Our model consists of 3 blocks: (1) a generator, (2) a discriminator, (3) an inference block.
1 code implementation • 8 Jan 2020 • Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar
In addition, patient recruitment can be difficult by the fact that clinical trials do not aim to provide a benefit to any given patient in the trial.
3 code implementations • ICLR 2020 • Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar
Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions.
1 code implementation • NeurIPS 2020 • Ioana Bica, James Jordon, Mihaela van der Schaar
While much attention has been given to the problem of estimating the effect of discrete interventions from observational data, relatively little work has been done in the setting of continuous-valued interventions, such as treatments associated with a dosage parameter.
1 code implementation • 23 Jul 2020 • James Jordon, Daniel Jarrett, Jinsung Yoon, Tavian Barnes, Paul Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing.
1 code implementation • NeurIPS 2020 • Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar
We also introduce a novel tabular data augmentation method for self- and semi-supervised learning frameworks.
no code implementations • NeurIPS 2020 • Jeroen Berrevoets, James Jordon, Ioana Bica, alexander gimson, Mihaela van der Schaar
Transplant-organs are a scarce medical resource.
no code implementations • 8 Dec 2020 • James Jordon, Alan Wilson, Mihaela van der Schaar
Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data.
1 code implementation • 4 Feb 2022 • Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar
However, no imputation at all also leads to biased estimates, as missingness determined by treatment introduces bias in covariates.
no code implementations • 6 May 2022 • James Jordon, Lukasz Szpruch, Florimond Houssiau, Mirko Bottarelli, Giovanni Cherubin, Carsten Maple, Samuel N. Cohen, Adrian Weller
This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy.
2 code implementations • 12 Nov 2022 • Florimond Houssiau, James Jordon, Samuel N. Cohen, Owen Daniel, Andrew Elliott, James Geddes, Callum Mole, Camila Rangel-Smith, Lukasz Szpruch
We here present TAPAS, a toolbox of attacks to evaluate synthetic data privacy under a wide range of scenarios.